Eigenface

CHAPTER 1 FACIAL RECOGNITION SYSTEM 1.1 INTRODUCTION A biometrics is, “Automated methods of recognizing an individual based on their unique physical or behavioral characteristics.” For example face, fingerprint, signature, voice etc. Face recognition is a task humans perform remarkably easily and successfully. In face recognition Features extracted from a face are processed and compared with similarly processed faces present in the database. If a face is recognized it is known or the system may

SECURITY SURVEILLANCE SYSTEM Security Through Image Processing Prof.Vishal Meshram,Jayendra More Department of Electronics & Telecommunication Engineering (ExTC), University Of Mumbai Vishwatmak Om Gurudev College Of Engineering Maharashtra State Highway 79, Mohili, Maharashtra 421601, India. Vishalmmeshram19@gmail.com, more.jayendra@yahoo.in Bhagyesh Birari,Swapnil Mahajan Department of Electronics & Telecommunication Engineering (ExTC), University Of Mumbai Vishwatmak Om Gurudev College Of

projection value of the input image is also found and the distance from the known projection values is calculated to identify who the individual is.Neural Network Based Face Recognition procedure is followed for forming the eigenvectors as in the Eigenface approach, which are then fed into the Neural Network Unit to train it on those vectors and the knowledge gained from the training phase is subsequently used for recognizing new input images. The training and recognition phases can be implemented

PRELIMINARIES This section expands a brief explanation of the basic frame-work of the principal component analysis and fuzzy logic, along with some of the key basic concepts. A. The principal component analysis (PCA) The Principal component analysis (PCA) is an essential technique in data compression and feature reduction [13] and it is a statistical technique applied to reduce a set of correlated variables to smaller uncorrelated variables to each other. PCA is considered as special transformation

This paper presents a image fusion technique based on PCA and fuzzy logic. the framework of the proposed image fusion technique is divided in the following major phases:  preprocesing phase  Feature extraction based on the principal component analysis  The image fusion based on fuzzy set  Reconstruction final image The figure (1) shows the framework of the proposed image fusion and its phases. Fig. 1. The proposed approach of image fusion phases A. Preprocessing Phase This phase consists of three

In this paper we present an analysis of face recognition system with a combination of Neural networks withSub-space method of feature extraction. Here we are considering both single layer such as Generalized Regression neural network (GRNN) and Multi layer such as Learning Vector quantization (LVQ). The analysis of these neural networks are done between feature vectors with respect to the recognition performance of subspace methods, namely Principal Component Analysis (PCA) and Fisher Linear Discriminant

FACE RECOGNITION USING ORTHOGONAL LOCALITY PRESERVING PROJECTIONS. Dr. Ravish R Singh Ronak K Khandelwal Manoj Chavan Academic Advisor EXTC Engineering EXTC Engineering Thakur Educational Trust L.R.Tiwari COE Thakur COE Mumbai, India. Mumbai,India. Mumbai, India. ravishrsingh@yahoo.com ronakkhandelwal2804@gmail.com prof.manoj@gmail.com Abstract: In this paper a hybrid technique is used for determining the face from an image. Face detection is one of the tedious

As the name suggests, there are no basic guidelines for these algorithms, hence it is unsupervised. These algorithms can be used to discover various pattern, divide the data into various clusters, reducing the dimensionality of the dataset for viewing, which may help researchers in better understanding of the physics of the problem. Here, an expert needs to be careful while choosing a certain algorithm and associated parameters for a specific case. Additionally, an expert needs to be very careful

ABSTRACT: Face is a analyzable multi-dimensional visual model and processing a process model for face recognition is challenging. This paper presents a methodological analysis for face identification based on content explanation formulation of coding and decoding the face image. categorization using the Euclidian distance. The content is to use the system for a particular face and separate from a large number of stored faces with some real time variations as well. The Eigen face attack uses particular

Technique Description Performance Evaluation Matrices Reference Markovian stochastic mixture approach It is composed of three main sections which includes face detection, face alignment and face recognition. Usually these sections are executed in bottom up approach. CSU Face Identification Evaluation System is used to evaluate the performance of the technique and it is found that bottom up approach proposed has better identification rate tested on 104 images. [9] orthogonal locality preserving projection